For all questions of non-miscellaneous type, only a small subset of the candidates are marked appropriate.

our model performs worse than the oracle for questions seeking persons and thingnames.


In this work, we propose a supervised machine learning approach to relation extraction which is applicable even when (deep) linguistic processing is not available or reliable.

we explore a kernel-based approach based solely on shallow linguistic processing, such as tokeniza-tion, sentence splitting, Part-of-Speech (PoS) tagging and lemmatization.


The experimental results show that the combined kernel K SL outperforms the basic kernels K GC and K LC on both data sets.

precision significantly increases at the expense of a lower recall.


presence of the relation and roles of the interacting entities), they perform reasonably well even when considered separately.

k GC achieved good performance on both data sets.


Constraints on possible scopal relationships between EPs may be explicitly specified in the grammar via relationships between holes and labels.

qeq constraints (the only type considered here) indicate that, in the scoped forms, a label must either plug a hole directly or be connected to it via a chain of quantifiers.


8 DMRS indirectly preserves the information about constituent structure which is essential for semantic interpretation, unlike some syntactic dependency schemes.

it retains information about a quantifier's N , since this forms the restrictor of the generalised quantifier (for instance Most white cats are deaf has different truth conditions from Most deaf cats are white).


Part of what is presented in this paper has been inspired by collaborative work with colleagues.

i would like to thank Willemijn Heeren, Roeland Ordelman and Stef Scagliola for their role in the genesis of ideas and insights.


In fact, we did experiment with a standard trigram LM, but found that it did not improve performance.

we trained two LMs, one on the polar subjective phrases and another on the neutral subjective phrases.


For future work, it would be interesting to do subjectivity and intensity classification using the same scheme and features.

for the task of subjectivity analysis, we speculate that the imagery score might be useful for tagging chunks with "subjective" and "objective" instead of positive, negative, and neutral.


Each run of the BFS calculates the minimum distance paths between v i and the rest of concepts of G KB .

we are interested in the minimum distance paths between v i and the concepts associated to the rest of the words in the context, v j ∈ j =i Concepts j .


Note that it was not our aim to improve the results of the basic classifiers on this scenario, but given the fact that we are going to apply all these techniques in the domain adaptation scenario, we need to show these results as baselines.

in the next section we will try to obtain results which improve significantly over the best results in this section.


On the one hand, clique-based methods (see §2.3 for Figure 1: General description of our system details on cliques) are interesting as they allow the same NE to be in different cliques.

cliques allow to represent the different possible annotations of a NE.


The general term of this similarity matrix is: S(cli k , cli k ) = S kk = cli k , cli k .

then, we want to maximize the following clustering function: cli k and cli k have more chances to be in the same cluster providing that their similarity measure, S kk , is greater or equal to the mean average of positive similarities.


Given a NE e i and a syntactic context c j , we now introduce the contextual cluster assignment matrix A ctxt (e i , c j ) as follows: A ctxt (e i , c j ) = clu * where: clu * = Argmax {clu l :clu l e i ;Fe(e i ,clu l )>1} F c (c j , clu l ).

clu * is the cluster for which we find more than one occurrence of e i and the highest score related to the context c j .


In other words, clu * is the cluster for which we find more than one occurrence of e i and the highest score related to the context c j .

furthermore, we compute a default cluster assignment matrix A def , which does not depend on the local context: A def (e i ) = clu • where: clu • is the cluster containing the biggest clique cli k containing e i .


However, our aim is to show that the CBC-NER system is, despite its low performances alone, complementary to other basic NER systems.

we want to show that the exploitation of the CBC system's NE resource is beneficial and non-redundant compared to other baseline NER systems.


The input to the clustering algorithm are instances of the ambiguous word with their accompanying contexts (represented by co-occurrence vectors) and the output is a grouping of these instances into classes corresponding to the induced senses.

contexts that are grouped together in the same class represent a specific word sense.


The first idea that comes to mind, is to use the same model while treating various features as word-like elements.

we could simply assume that the contexts we wish to model are the union of all our features.


This is the purpose of the Gibbs sampling procedure.

in order to derive the update function used in the Gibbs sampler, we must provide the conditional probability of the i-th variable being assigned sense s i in layer l, given the feature value f i of the context variable and the current sense assignments of all the other variables in the data (s −i ): The probability of a single sense assignment, s i , is proportional to the product of the likelihood (of feature f i , given the rest of the data) and the prior probability of the assignment.


Feature Space Our experiments used a feature set designed to capture both immediate local context, wider context and syntactic context.

we experimented with six feature categories: ±10-word window (10w), ±5-word window (5w), collocations (1w), word n-grams (ng), part-ofspeech n-grams (pg) and dependency relations (dp).


Though preliminary, our results demonstrate that it is possible, using this method, to extract a high-precision and high-recall root word-form lexicon.

we show that with a POS tagger capable of labelling wordforms with POS categories at an accuracy of about 88%, we can extract root word-forms with an accuracy of about 87% and a precision and recall of 94.1% and 95.3% respectively.


These results should be approached with some caution.

it is possible that the results for the systems other than ours are underestimated due to errors in the evaluation.


This procedure is repeated for multiple query batches.

our scheme attempts to maximize the number of retrieved relevant documents, when two restrictions apply: (1) queries are not "free": each query costs some time or money; for instance, the number of queries submitted within a particular period of time is limited, and (2) the number of documents retrieved for a particular query is limited to a particular number of "top hits".


Although we do not have the space to review the many alignment systems that have already been proposed, we will shortly refer to works that share some similarities with our approach.

the first alignment model we will present has already been described in (Melamed, 2000).


We will now try to add a distortion model; however, rather than directly modeling the movement of the positions of the words, as is the case in the IBM models, we will try to design a distortion model based on the structures of the sentences.

we are interested in using the trees produced by syntactic parsers.


Finally, although we introduced the P-sets as being generated from a syntactic tree, they do not need to.

we found interesting to use P-sets consisting of every pair of adja-cent positions in a sentence.


There are however some surprising results.

sDM:Adjacency produced surprisingly good results.


Our approach is based on the observation that COREF's contribution tracking can be viewed as assigning a fate to every dialogue state it entertains as part of some thread of interpretation.

if we consider the agent's contribution tracking retrospectively, every dialogue state can be assigned a fate of correct or incorrect, where a state is viewed as correct if it or some of its descendants eventually capture all the probability mass that COREF is distributing across the viable surviving states, and incorrect otherwise.


Therefore, to evaluate our approach, while making maximal use of our available data set, we performed a hold-one-subject-out cross-validation using our 20 human subjects H = {h 1 , ..., h 20 }.

for each subject h i , we trained a model on the training examples associated with subjects H \ {h i }, and then tested the model on the examples associated with subject h i .


Nevertheless, we believe that these results showcase how judicious system-building efforts can lead to dialogue capabilities that defuse some of the bottlenecks to learning rich pragmatic interpretation.

a focus on improving our agents' basic abilities to tolerate and resolve ambiguities as a dialogue proceeds may prove to be a valuable technique for improving the overall dialogue competence of the agents we build.


(2008) and generate NIL labels for those pairs of words which also occur as a true labeled relation.

only word pairs which can be relations can also be NILs.


As described in section 5.1, we can use ambiguity classes to constrain the output of a model.

we take models 7 and 8 and constrain each selected tag to be one which is within the ambiguity class of a lexical model, either 1 or 3.


Specifically, we take models 7 and 8 and constrain each selected tag to be one which is within the ambiguity class of a lexical model, either 1 or 3.

if the TiMBL-determined label is not in the ambiguity class, we select the most likely tag of the ones which are.


Using the same notation proposed in (Moschitti et al., 2007) and , our SCLM trains joint probability P (W, C) of word and concept sequences from an annotated corpus: where Since we use a 3-gram conceptual language model, the history h i is All the steps of the translation process described here and above are implemented as Finite State Transducers (FST) using the AT&T FSM/GRM tools and the SRILM (Stolcke, 2002) tools.

the SCLM is trained using SRILM tools and then converted to an FST.


For the second aspect, we focus on applying these rules to the RTE task.

we use a structure representation derived from the dependency parse trees of T and H, which aims to capture the essential information they convey.


We represent verbs (i.e., frame evoking elements) in the seed and unlabeled corpora by their predicate-argument structure.

we record the direct dependents of the predicate course (e.g., blood or again in Figure 1) and their grammatical roles (e.g., SUBJ, MOD).


More formally, given a labeled predicate-argument structure p l with m arguments and an unlabeled predicate-argument structure p u with n arguments, we consider (and score) all possible alignments between these arguments.

a (partial) alignment can be viewed as an injective function σ : an argument i of p l is aligned to argument σ(i) of p u if i ∈ M σ .


We define the syntactic measure on the grammatical relations produced by RASP.

we set syn(g l i , g u σ(i) ) to 1 if the relations are identical, to a ≤ 1 if the relations are of the same type but different subtype 2 and to 0 otherwise.


We evaluated our method on a semantic role labeling task.

we compared the performance of a generic semantic role labeler trained on the seed corpus and a larger corpus expanded with annotations produced by our method.


Classical Chinese is in general monosyllabic, with each syllable corresponding to an independent morpheme that can be visually rendered with a written character.

characters did represent the basic syntactic unit in Classical Chinese, and thus became the sociologically intuitive notion.


(2007a) label potential addressees in terms of the order in which they speak after the you-utterance.

for a given you-utterance, the potential addressee who speaks next is labeled 1, the potential addressee who speaks after that is 2, and the remaining participant is 3.


We present parsing algorithms for various mildly non-projective dependency formalisms.

algorithms are presented for: all well-nested structures of gap degree at most 1, with the same complexity as the best existing parsers for constituency formalisms of equivalent generative power; all well-nested structures with gap degree bounded by any constant k; and a new class of structures with gap degree up to k that includes some ill-nested structures.


Kuhlmann (2007) investigates several such classes, based on well-nestedness and gap degree constraints (Bodirsky et al., 2005), relating them to lexicalised constituency grammar formalisms.

he shows that: linear context-free rewriting systems (LCFRS) with fan-out k (Vijay-Shanker et al., 1987;Satta, 1992) induce the set of dependency structures with gap degree at most k − 1; coupled context-free grammars in which the maximal rank of a nonterminal is k (Hotz and Pitsch, 1996) induce the set of well-nested dependency structures with gap degree at most k − 1; and LTAGs (Joshi and Schabes, 1997) induce the set of well-nested dependency structures with gap degree at most 1.


Note that the item and deduction step sets of an MG k parser are always supersets of those of WG k .

the steps for WG k are those obtained from strings that do not contain abab or baba as a scattered substring.


This work presents multiple novel approaches for automatically extracting biographic facts such as "Birthdate", "Occupation", "Nationality", and "Religion", making use of diverse sources of information present in biographies.

we have proposed and evaluated the following 6 distinct original approaches to this task with large collective empirical gains: 1.


However, they are sometimes distributed across the stem: different parts of a stem may be constrained by the presence of a particular feature.

for example, the feature +ps (abbreviating [ps=True]) causes the gemination of the stem-initial consonant under various circum- stances and also controls the final vowel in the stem in the imperfective, and the feature +tr is marked by the vowel a before the first root consonant and, in the imperfective, by the nature of the vowel that follows the first root consonant (E where we would otherwise expect 1, 1 where we would otherwise expect E.) as with the verb affixes, there are long-distance dependencies within the verb stem.


and the FS [tam=imprf, der=[+ps,-tr,-rc,-it], sbj=[+2p,+plr,fem], +neg, obj=nil, -rel] (see Figure 3).

this word represents the imperfective, negative, non-relativized passive of the verb root √ flt .


The semiring properties fail to hold when introducing non-local features in this way.


From this corpus, we are able to perform experiments on person identification.

this means recognizing which individual of a set of people composed a document or spoke an utterance which was transcribed.


We believe using text and transcribed speech in this manner is a novel research area.

the following types of experiments can be performed: -Identification of person in a novel genre (using five genres as training) -Identification of person in a novel topic (using five topics as training) -Identification of person in written genres, after training on the two spoken genres -Identification of person in spoken genres, after training on the written genres -Identification of person in written genres, after training on the other written genres In this paper, we discuss the formation and statistics of this corpus and report results for identifying individual people using techniques that utilize several different feature sets.


Therefore, while the interpreter finds it easier to deal with these excerpts (known vocabulary among others) and L043-3 seems to be more complicated (domain-specific, technical aspect), the automatic system finds it more complicated with the former and less with the latter.

the interpreter has to "understand" what is said in L043-3, contrary to the automatic system, in order to translate.


Second, C&S identify potential NEs based on proper nouns, but unlike English, (1) proper noun identification for Bengali is non-trivial, due to the lack of capitalization; and (2) there does not exist an accurate Bengali part-of-speech (POS) tagger for providing such information, owing to the scarcity of annotated data for training the tagger.

bengali NER is complicated not only by the scarcity of annotated data, but also by the lack of an accurate POS tagger.


Second, they typically adopt a pipelined NER architecture, performing POS tagging prior to NER and encoding the resulting not-so-accurate POS information as a feature.

errors in POS tagging are propagated to the NE recognizer via the POS feature, thus limiting its performance.


Motivated in part by these weaknesses, we in-vestigate how to improve a learning-based NE recognizer that does not rely on manually-constructed gazetteers.

we investigate two learning architectures for our NER system.


We use these categories to help annotate the title of an article.

if an article has a category whose name starts with "Born on" or "Death on," we label the corresponding title with PER.


Similarly, if it has a category whose name starts with "Cities of" or "Countries of," we NE Class Keywords PER "born," "died," "one," "famous" LOC "city," "area," "population," "located," "part of" ORG "establish," "situate," "publish" If an article does not belong to one of the four categories above, we label its title with the help of a small set of seed keywords shown in Table 1.

for each of the three NE classes shown on the left of Table  1, we compute a weighted sum of its keywords: a keyword that appears in the first paragraph has a weight of 3, a keyword that appears elsewhere in the article has a weight of 1, and a keyword that does not appear in the article has a weight of 0.


We can now use the title list to annotate a text.

we process each word w in the text in a left-to-right manner, using the following steps: 1.


The recognizer adopts a traditional architecture, assuming that POS tagging is performed prior to NER.

the NE recognizer will use the POS acquired in Section 5 as one of its features.


Corpus The corpus we used for NER evaluation is the same as the one described in the previous POS of current word t i POS of previous word section.

in addition to POS information, each sentence in the corpus is annotated with NE information.


We view NE recognition as a sequence labeling problem.

we combine NE identification and classification into one step, labeling each word in a test text with its NE tag.


In contrast, we propose a relatively simple model for jointly learning Bengali POS tagging and NER, by exploiting the limited dependencies between the two tasks.

we make the observation that most of the Bengali words that are part of an NE are also proper nouns.


Otherwise, its class is its NE tag, which can be PER, ORG, LOC, or OTHERS.

our joint model exploits the observation that we made earlier in the section by assuming that only proper nouns can be part of a named entity.


Comparing these three experiments with their counterparts in Table 6, we can see that, except for the baseline, jointly modeling offers a significant improvement of 3.3% in overall F-measure.

10 the joint model benefits significantly from our 10 The POS tagging results are not shown due to space limitations.


As we can see from the partial POS lexicon for English in Figure 1, "the" is unambiguous with respect to POS tagging, since it can only be a determiner (DT), whereas "sting" is ambiguous, since it can be a common noun (NN), a proper noun (NNP) or a verb (VB).

the lexicon imposes constraints on the possible POS tags  Conceivably, tagging accuracy decreases with the increase in ambiguity: unambiguous words such as "the" will always be tagged correctly; on the other hand, unseen words (or words not present in the POS lexicon) are among the most ambiguous words, since they are not constrained at all and therefore can receive any of the POS tags.


This assumption, however, is fairly unrealistic in practice, as a perfect POS lexicon can only be constructed by having a linguist manually label each word in a language with its possible POS tags.

1 the labor-intensive POS lexicon construction process renders unsupervised POS taggers a lot less unsupervised than they appear.


This generation process repeats until the end of the word sequence is reached.

the parameters of an HMM, θ, are composed of a set of statespecific (1) output distributions (over word tokens) and (2) transition distributions, both of which can be learned using the EM algorithm.


To prevent over-generalization and at the same time exploit suffixes, we propose as our first extension to G&G's framework a hybrid approach to word/suffix emission: a word is emitted if it is present in the word-based POS lexicon; otherwise, its suffix is emitted.

our approach imposes suffix-based constraints on the tagging of words that are unseen w.r.t.


ing a state-specific probability distribution over induced suffixes.

if the current word is present in the word-based POS lexicon, or if we cannot find any suffix for the word using the induced suffix list, then we emit the word.


This extension requires that we slightly modify the inference procedure.


Otherwise, it attempts to label w i based on its context.

if (w i−2 , w i−1 ), the word bigram preceding w i , appears in L (line 3), then w i is tagged according to D 1 (w i−2 , w i−1 ).


Corpus Our evaluation corpus is the one used in the shared task of the IJCNLP-08 Workshop on NER for South and South East Asian Languages.

4 we use the portion of the Bengali dataset that is manually POS-tagged.


We collapse the set of 26 POS tags into 15 tags.

while we retain the tags corresponding to the major POS categories, we merge some of the infrequent tags designed to capture Indian language specific structure (e.g., reduplication, echo words) into a category called OTHERS.


Thus, some research has been focused on deriving different word-sense groupings to overcome the fine-grained distinctions of WN (Hearst and Schütze, 1993), , (Mihalcea and Moldovan, 2001), (Agirre and LopezDeLa-Calle, 2003), (Navigli, 2006) and (Snow et al., 2007).

they provide methods for grouping senses of the same word, thus producing coarser word sense groupings for better disambiguation.


In contrast, some research have been focused on using predefined sets of sense-groupings for learning class-based classifiers for WSD (Segond et al., 1997), (Ciaramita and Johnson, 2003), (Villarejo et al., 2005), (Curran, 2005) and (Ciaramita and Altun, 2006).

grouping senses of different words into the same explicit and comprehensive semantic class.


Topical features: word-forms or lemmas appearing in windows around the target word.

our systems use the following basic features: Word-forms and lemmas in a window of 10 words around the target word PoS: the concatenation of the preceding/following three/five PoS Bigrams and trigrams formed by lemmas and word-forms and obtained in a window of 5 words.


Several types of semantic classes have been considered to create these features.

two different sets of BLC (BLC20 and BLC50 9 ), SuperSenses, WordNet Domains (WND) and SUMO.


These BLC sets were built using all kind of relations.

10 the value of the feature, for example a feature type can be word-form, and a feature of that type can be "houses" of the class), and also obtain the total frequency of the feature, for all the classes.


As expected according to most literature in WSD, the performances of the MFC baselines are very high.

those corresponding to nouns (ranging from 70% to 80%).


While for SE3 none of the system presents a significant improvement over the baselines, for SE2 a significant improvement is obtained by using several types of seman-tic features.

when using WordNet Domains but also BLC20.


Regarding verbs, the system obtains significant improvements over the baselines using different types of semantic features both in SE2 and SE3.

when using again WordNet Domains as semantic features.


The training corpus has been divided in portions of 5% of the total number of files.

complete files are added to the training corpus of each incremental test.


In SE3, the system again only improves the F1 measure around 3% while increasing the training corpus from 30% to 100% of SemCor.

most of the knowledge required for the class-based WSD system seems to be already present on a small part of SemCor.


In SE3, the system again only improves the F1 measure around 2% while increasing the training corpus from 30% to 100% of SemCor.

with only 25% of the whole corpus, the class-based WSD system reaches a F1 close to the performance using all corpus.


We apply our method to the most recent version of Cornetto (Vossen et al., 2007), an extension of the Dutch WordNet, and we experiment with various parameters of the algorithm, in order to arrive at a good setting for porting the method to other languages.

we evaluate the quality of the resulting Dutch subjectivity lexicon using different subsets of wordnet relations and information in the glosses (definitions).


More generally, we believe that the method we describe here can be easily adapted to other language pairs, especially those for which few resources are available.

we did not have access to a significant parallel corpus, and most of the resources we used are readily available for many other languages.


Our work consists of of two sub-tasks: Identifying TTTs and then transliterating them.

we use the following resources for this work: For the identification task we use a large un-annotated corpus of articles from Hebrew press and webforums (Itai and Wintner, 2008) consisting of 16 million tokens.


The first observation we made when running our algorithm was that in more than 90% of all cases, Balas-First already constitutes the optimal solution.

the time-consuming search for a less expensive solution ended without further success.


As can be seen from the figure, the function (at least empirically) is rather erratic.

searching for the optimal solution beyond Balas-First does not seem to lead reliably (and monotonically) to better F-measure values.


In the tree, it is the anchors that are mapped to the nodes with the labels fat j 1 and cat n 1; the label is mapped to the conjunction node just above them.

the role of the anchor in an EP is to connect a predicate to its arguments, while the role of the label is to connect the EP to the surrounding formula.


Notice that one RMRS may be satisfied by multiple trees; we can take the RMRS to be a partial description of each of these trees.

rMrSs may represent semantic scope ambiguities and/or missing information about semantic dependencies, lexical subcategorisation and lexical senses.


Another feature of the existing RMRS literature is that each term of an RMRS is equipped with a sort.

individual variables x, event variables e and holes h are arranged together with their subsorts (e.g., e past ) and supersorts (e.g., sort i abstracts over x and e) into a sort hierarchy S. For simplicity we defined RMRS without sorts, but it is straightforward to add them.


However, there are limits to our understanding of the formal properties of CCG and its relation to other grammar formalisms.

while it is well-known that CCG belongs to a family of mildly context-sensitive formalisms that all generate the same string languages (Vijay-Shanker and Weir, 1994), there are few results about the strong generative capacity of CCG.


We then explore the strong generative capacity of PF-CCG in terms of dependency trees.

we cast new light on the relationship between CCG and other mildly context-sensitive formalisms such as Tree-Adjoining Grammar (TAG; Joshi and Schabes (1997)) and Linear Context-Free Rewrite Systems (LCFRS;Vijay-Shanker et al.


As a final aside, we note that the construction for extracting purely applicative derivations from the terms described by the RTG has interesting consequences for the weak generative capacity of PF-CCG.

it has the corollary that for any PF-CCG derivation δ over a string w, there is a permutation of w that can be accepted by a PF-CCG derivation that uses only application-that is, every string language L that can be generated by a PF-CCG grammar has a context-free sublanguage L such that all words in L are permutations of words in L .


All these results were technically restricted to the fragment of PF-CCG, and one focus of future work will be to extend them to as large a fragment of CCG as possible.

we plan to extend the lambda notation used in Figure 3 to cover typeraising and higher-order categories.


The accuracy rate for the original word graphs in the data set (last row in Table 1) is 66.67 which indicates that only 142 out of 213 contain the reference sentence.

in 71 of the instances, the reference sentence is not included in  Figure 6: WER for HUB-1 first-best hypotheses obtained using different language-model scaling factors and α = 1.


The goal of the final assembly of these two components was to achieve an enhanced Speech Translation (ST) system.

we propose to parse the word graph generated by the SR module inside the rule-based parser.


The approach rates comparably across the 10 translation directions we considered.

we do not see a drop in performance when translating into a morphology rich language (such as Finnish), or when translating into languages with different scripts.


DMA is stricter than CER in the sense that it does not allow for partial matches in the semantic representation.

if the classified utterance corresponds to the same semantic representation as the transcribed then we have 100% DMA, otherwise 0%.


The 50% value for the WER as a threshold for the distinction between the 'pos' and 'neg' category is adopted from (Gabsdil, 2003), based on the fact that WER is affected by concept accuracy (Boros et al., 1996).

if a hypothesis is erroneous as far as its transcript is concerned   The baseline for our experiments is the behavior of the Town-Info spoken dialogue system that was used to collect the experimental data.


We used a combination of acoustic features and features computed from dialogue context to predict the quality of incoming recognition hypotheses to an SDS.

we use a score computed from a simple statistical User Simulation, which measures the likelihood that the user really said each hypothesis.


Again mirroring our results, their analysis indicates that even though there is no overall difference, there are situations where one system generally outperforms the other.

carenini and cheung (2008) show that an entity's controversiality, e.g., mid-range star rating, is correlated with which summary has highest value.


When this assumption fails, one can parse opinions at a finer-level (Jindal and Liu, 2006;Stoyanov and Cardie, 2008) In this study, we look at an extractive summarization setting where S is built by extracting representative bits of text from the set D, subject to pre-specified length constraints.

assume each document d i is segmented into candidate text excerpts.


This method is common among sentiment analysis systems (Hu and Liu, 2004a;Kim and Hovy, 2004;Blair-Goldensohn et al., 2008).

we use the lexicons that were created and evaluated by Blair-Goldensohn et al.


These statements carry no additional information over a product's overall star rating; 4) Raters did recognize (and strongly disliked) when the overall sentiment of the summary was inconsistent with the star rating; 5) Raters tended to prefer different systems depending on what the star rating was.

the SMAC system was generally preferred for products with neutral overall ratings, whereas the SAM system is preferred for products with ratings at the extremes.


Each input point x k i ∈ R m is a feature vector representing the properties of that particular item relative to the query.

the goal is to learn a scoring function s( a ranking SVM learns a scoring function whose induced ranking over data points respects all preferences in the training data.


This raises an interesting line of future research: can we iterate this process to build even better summarizers?

can we use this trained summarizer (and variants of it) to generate more examples for raters to judge, and then use that data to learn even more powerful summarizers, which in turn could be used to generate even more training judgments, etc.


The taggers TnT* and Ice* benefit the most from the corpus error correction -their   Since the TnT tagger is a data-driven tagger, it is interesting to see whether the corrected corpus changes the language model (to the better) of the tagger.

does retraining using the corrected corpus produce better results than using the language model generated from the original corpus?


If we replace a single rule in the derivation, the partial score of the rest of derivation is also affected, because the terms e j−n+1 ...e j may come from more than one rule.

this parameterization encodes a dependency between the steps in a derivation.


The second measure is the alignment confidence (Ma et al., 2007), defined as where C(a i ) denotes the number of alignments proposed by the word aligner that are identical to a i .

aC(a i ) measures how often the aligner aligns C i and e i when they co-occur.


For the dialogue data, we used the Chinese-English datasets provided within the IWSLT 2007 evaluation campaign.

we used the standard training data, to which we added devset1 and devset2.


We address the grading problem from a text similarity perspective and examine the usefulness of various textto-text semantic similarity measures for automatically grading short student answers.

in this paper we seek answers to the following questions.


3 Our experiments are centered around the use of measures of similarity for automatic short answer grading.

we carry out three sets of experiments, seeking answers to the following three research questions.


One of the key considerations when applying corpus-based techniques is the extent to which size and subject matter affect the overall performance of the system.

based on the underlying processes involved, the LSA and ESA corpusbased methods are expected to be especially sensitive to changes in domain and size.


Next, we use our best system (LSA trained on domainspecific data with relevance feedback), and run a ten-fold cross-validation on the data set.

for each fold, the system uses the remaining nine folds to automatically identify a threshold to maximize the matching with the gold standard.


(Collins and Duffy, 2002;Kudo and Matsumoto, 2003;Cumby and Roth, 2003;Shen et al., 2003;Moschitti and Bejan, 2004;Culotta and Sorensen, 2004;Kudo et al., 2005;Toutanova et al., 2004;Kazama and Torisawa, 2005;Zhang et al., 2006;.

in question classification, tree kernels, e.g.


Moreover, (Shen and Lapata, 2007;Surdeanu et al., 2008;Chali and Joty, 2008) have shown that shallow semantic information in the form of Predicate Argument Structures (PASs) (Jackendoff, 1990;Johnson and Fillmore, 2000) improves the automatic detection of correct answers to a target question.

in  kernels for the processing of PASs (in PropBank 1 format (Kingsbury and Palmer, 2002)) extracted from question/answer pairs were proposed.


Our experiments aim at studying the impact of our kernels applied to syntactic/semantic structures for the detection of relations between short texts.

we first show that our SRK is far more efficient and effective than SSTK.


Our learning framework is constituted by Support Vector Machines (SVMs) and kernel methods applied to automatically generated syntactic and semantic structures.

we designed (i) a new Semantic Role Kernel (SRK) based on a very fast algorithm; (ii) a new sequence kernel over POS tags to encode shallow syntactic information; (iii) many kernel combinations (to our knowledge no previous work uses so many different kernels) which allow for the study of the role of several linguistic levels in a well defined statistical framework.


Further, in (Mukherjee et al., 2007), the authors define the Phoneme-Phoneme Network or PhoNet as the one-mode projection of PlaNet onto the consonant nodes, i.e., a network G = V C , E cc , where the nodes are the consonants and two nodes v c and v c are linked by an edge with weight equal to the number of languages in which both c and c occur together.

phoNet can be expressed as a matrix B (see Fig.


The numerically low rank of PhoNet suggests that there are certain prototypical structures that frequently repeat themselves across the consonant inventories, thereby, increasing the number of 0 eigenvalues to a large extent.

all the rows of the matrix B (i.e., the inventories) can be expressed as the linear combination of a few independent row vectors, also known as factors.


English/Italian) section of the dictionary, s i is a sense from the target section for i ≤ k and from the source section for i > k, for some k such that 0 ≤ k ≤ n − 2.

the DFS can jump at any time from the target section to the source section.


Among the methods, the cross reference heuristic is the closest technique to our notion of cycles and quasi-cycles.

given a pair of words w and w , this heuristic is based on the occurrence of w in the gloss of a sense s of w and, vice versa, of w in the gloss of a sense s of w. a graph cycle s → s → s of length 2 is sought.


The CoNLL NER shared tasks (Tjong Kim Sang, 2002;Tjong Kim Sang and De Meulder, 2003)  One challenge for NER research is establishing an appropriate evaluation metric (Nadeau and Sekine, 2007).

entities may be correctly delimited but mis-classified, or entity boundaries may be mismatched.


There is much room for improving the results of our Wikipedia-based NE annotations.

a more careful approach to link inference may further reduce incorrect boundaries of tagged entities.


O Séaghdha and Copestake (2008) demonstrate how standard techniques for distributional similarity can be implemented in a kernel framework.

kernels for comparing probability distributions can be derived from standard probabilistic distance measures through simple transformations.


Third, we show that large-scale, automatically-derived classes of in-stances can have as much as, or even bigger, practical impact in open-domain information extraction tasks than similar data from large-scale, highcoverage, manually-compiled resources.

evaluation results indicate that the accuracy of the extracted lists of attributes is higher by 8% at rank 10, 13% at rank 30 and 18% at rank 50, when using the automatically-extracted classes of instances rather than the comparatively more numerous and a-priori more reliable, humangenerated, collaboratively-vetted classes of instances available within Wikipedia (Remy, 2002).


Third, in the leftmost graphs, the more restrictive run E s has lower precision scores across all ranks than its less restrictive counterpart E a .

adding more Table 5: Comparative accuracy of the attributes extracted by various runs, for individual concepts, as an average over the entire set of 25 target concepts, and as an average over (variable) subsets of the 25 target concepts for which some attributes were extracted in each run.


The log-likelihood ratio gives the highest number of associations at rank 1 for both classes.

surprising is its strong performance with respect to semantic similarity, since this relation is only a side effect of collocation.


We want to learn some weight vector w such that w • Φ(c i ) > w • Φ(c j ) holds for all pairs c i and c j that have the relation c i < r c j .

we want w • (Φ(c i ) − Φ(c j )) > 0 and we can use some margin in the place of 0.


We try several graph-based semi-supervised learning methods like Mincuts, Randomized Mincuts, and Label Propagation.

we define a graph with nodes consisting of the words or phrases to be classified either as positive or negative.


We now investigate the performance of the various methods on the number of seeds used.

we are interested in performance under conditions when the number of seeds are few -which is the motivation for using semi-supervised learning in the first place.


In this section we will describe an entropy based algorithm, which aims at learning the simplest grammar fitting the data.

we take a "supertagging" perspective (Bangalore and Joshi, 1999) and aim at reducing the uncertainty about which elementary tree (supertag) to assign to a given lexical item.


The main intuition behind our second method is that we like to assign heads to a tree t in such a way that the elementary trees that we can extract from t are frequently observed in other trees as well.

we like to use elementary trees which are general enough to occur in many possible constructions.


A simple strategy to ensure full coverage is to smooth with the treebank PCFG.

we add to our grammars all CFG rules that can be extracted from the training corpus and give them a small weight proportional to their frequency 3 .


Note that we are assuming token identity here, and not for example copying of data structures.

we assume that it indeed is the same IU that is in the left and right buffers of connected modules.


Formally, we define IUs as tuples IU = I, L, G, T , C, S, P , where • I is an identifier, which has to be unique for each IU over the lifetime of a system.

(at no point in the system's life can there be two or more IUs with the same ID.)


In a c:in=out-type module, the most complete RB-IU (or set of RB-IUs) is only as complete as the most complete (set of) LB-IU(s).

the module does not speculate about completions, nor does it lag behind.


Figure 2 illustrates that new H * L accents are realised more variably than given ones.

again, an increase in frequency of occurrence co-occurs with an increase in similarity, this time at the level of information status.


This indicates that under the condition of novelty the frequency of occurrence effect is more pronounced.

there is a considerably more noticeable difference across the distributions of L * H, H * L and H * , when nov- elty is considered: novelty compounds the frequency of occurrence effect.


the idea that frequently occurring behaviours undergo processes of entrenchment, in Experiment 1 one might expect to see greater similarity in the realisations of L * H. However, it is important to note that while tokens of L * H are not particularly similar to each other (the bulk of the distribution is around zero (see figure 1)), they are not too dissimilar either.

they rest at the midpoint of the similarity continuum produced by cosine calculation, in quite a normal looking distribution.


Distributions of given and new H * L similarity scores differed significantly, as did distributions of given and new L * H similarity scores, indicating that information status affects realisation.

for both pitch accent types, given and new tokens behave differently.


The last row in Table 3 shows the results of classifying the training data.

it shows the results of classifying one tenth of the data after a tenfold cross validation and can be considered an upper bound for the performance of this classifier on our evaluation data.


If a digit with a falling pitch is detected, the system plans an end-of-sequence utterance, but waits a little bit longer before producing it, to see if there really are no more words coming in.

the system bases its turn-taking decisions on a combination of ASR, prosody and silencethresholds, where the length of the threshold differs for different prosodic signals, and where reactions are planned already during the silence.


It can be seen that the performance of the classifier is generally relatively stable, with accuracies above 50% for most idioms.

12 the classifier performs well on both, expressions with a dominant non-literal meaning and those with a dominant literal meaning; it is not biased towards the non-literal class.


We have extended the averaged perceptron setup in the following way: the training algorithm is fed, in each iteration, by a concatenation of the supervised data (the manually tagged corpus) and the automatically pre-tagged unsupervised data, different for each iteration (in this order).

the training algorithm proper does not change at all: it is the data and their selection (including the selection of the way they are automatically tagged) that makes all the difference.


When n = 30, the F-measure has already reached its plateau, with the exactitude of 83.0%, and the inference time of 80 seconds.

the F-measure approached its plateau when n went to 30, with a high exactitude and a low inference time.


We represent text summarization as an optimization problem and attempt to globally solve the problem.

we represent text summarization as a maximum coverage problem with knapsack constraint (MCKP).


We conduct comprehensive comparative experiments of those algorithms.

we test the greedy algorithm, the greedy algorithm with performance guarantee, the stack decoding, the linear relaxation problem with randomized decoding, and the branch-andbound method.


The goal of text summarization is to cover as many conceptual units as possible using only a small number of sentences.

the goal is to find a subset S(⊂ D) that covers as many conceptual units as possible.


We regard word e j as covered when at least one sentence containing e j is selected as part of the summary.

word e j is covered if and only if ∑ i a ij x i ≥ 1.


We determined the value of λ for each method using DUC'03 as development data.

we conducted experiments on DUC'03 with different λ (∈ {0.0, 0.1, • • • , 1.0}) and simply selected the one with the highest ROUGE-1 value.


our parser could be located somewhere between traditional history-based approaches and whole-sentence approaches.


The CRF-based POS tagger is incorporated in the parser in exactly the same way as the other layers of chunking.

the POS tagging process is treated like the bottom layer of chunking, so the parser considers multiple probabilistic hypotheses output by the tagger in the search algorithm described in the previous section.


While the procedure given here is not tied to a particular formalism in representing semantic con-text, in our experiments we make use of propositional templates, based on the verbalizations arising from KNEXT logical forms.

a proposition F with m argument positions generates m templates, each with one of the arguments replaced by an empty slot.


W ⊆ W will stand for the set of words found to occupy a given slot (in the corpus employed), and D : N →W * is a function mapping a node to the words it (partially) sense dominates.

for all n ∈ N and w ∈ W , if w ∈ D(n) then there is at least one sense n ∈ S(w) such that n is an ancestor of n as determined through use of P. For example, we would expect the word bank to be dominated by a node standing for a class such as company as well as a separate node standing for, e.g., location.


In all cases our method gives precision results comparable or superior to the first-sense heuristic, while at all times giving higher recall.

for the case of Primary type, corresponding to the derived type that accounted for the largest number of observations for the given argument slot, our method shows strong performance across the board, suggesting that our derived abstractions are general enough to pick up multiple acceptable senses for observed words, but not so general as to allow unrelated senses.


The drawback of this method is, that in the case a hash collision would have occurred in an ordinary hash table, we now simply assume that the input tuple was in the table.

: the filter is potentially too permissive in such cases.


For that level of accuracy, the base-line model requires a mean CPU-time of about 25 seconds.

for the same level of accuracy, the prefix filter leads to a parser that is more than four times faster.


The tree is then traversed to obtain the final word ordering.

we present two spanning tree algorithms.


One limitation of using the CLE algorithm for generation is that the resulting tree, though maximal in probability, may not conform to basic linguistic properties of a dependency tree.

it may not have the correct number of arguments for each head word.


In particular, it may not have the correct number of arguments for each head word.

a word may have too few or too many modifiers.


One benefit to this evaluation is that content selection, as a factor, is held constant.

the probability of word selection is uniform for all words.


Additionally, the heads of base noun phrases were included in this input set.

we do not regenerate the base noun phrases.


PMI was used for measuring association, owing to its convenience and similarity to co-dispersion, but it should be noted that the specific formulation of the association measure is more-or-less irrelevant in the present context, where we are using relative association levels between a real and random corpus as a proxy for how much structural information is captured from the corpus.

put, the figures show the percentage of times a given association score or lower was measured between word types in a corpus which is known to be devoid of any actual syntagmatic association.


Further work is to be conducted in applying the method to a range of linguistic tasks, with an initial focus on lexical semantics.

properties of resultant word-space models and similarity measures beg a thorough investigation: while we would expect to gain denser higher-precision vectors, there might prove to be overriding qualitative differences.


Without Markovian feature like preact −1 , a shiftreduce parser can scan through an input sequence in linear time.

the decoding of a parsing method for word segmentation will be extremely fast.


• In order to reduce the sparsity of predicate argument fillers we consider replacing lemmas of their syntactic heads with word clusters induced by a clustering algorithm as a preprocessing step.

we use Brown (Br) clustering (Brown et al., 1992) induced over RCV1 corpus (Turian et al., 2010).


This result brings DS closer to the central concerns of FS.

the QN study is the first to our knowledge to show that DS vectors capture semantic properties not only of content words, but of an important class of function words (quantifying determiners) deeply studied in FS but of little interest until now in DS.


A few systems exploit deep FS analysis (Bos and Markert, 2006;Chambers et al., 2007).

the FS results about QP properties that affect entailment have been exploited by Chambers et al, who complement a core broad-coverage system with a Natural Logic module to trade lower recall for higher precision.


Corpus-harvested semantic vectors representing adjective-noun constructions and their heads encode a relation of entailment that can be exploited to train a classifier to detect lexical entailment.

a relation of feature inclusion between the narrower antecedent and broader consequent terms captures both AN |= N and N 1 |= N 2 entailment.


Rather, our interest is in evaluating semantic models in order to determine their suitability for inference tasks.

we contend that it is desirable and arguably necessary for a compositional semantic representation to support inference invariantly, in the sense that the particular syntactic construction that guided the composition should not matter relative to the representations of syntactically different phrases with the same meanings.


Our framework consists of the following components: a semantic model to be evaluated, pairs of sentences that are considered to have high similarity, and pairs of sentences that are considered to have low similarity.

the semantic model is a binary function, s = M(x, x ′ ), which returns a realvalued similarity score, s, given a pair of arbitrary linguistic units (that is, words, phrases, sentences, etc.


In one of them, E, the entities are in the target semantic relation, while in the other, N E, they are not.

the evaluation sets H and L are then constructed as follows: the high similarity sentence pairs are all the pairs where both express the target semantic relation, except the pairs between a sentence and itself, while the low similarity pairs are all the pairs where exactly one of the two sentences expresses the target relation.


These sentences illustrate the amount of syntactic and lexical variation that the semantic model must recognize as expressing the same semantic relation.

besides recognizing synonymy or near-synonymy at the lexical level, models must also account for subcategorization differences, extra arguments or adjuncts, and partof-speech differences due to nominalization.


In this task, the system is given a question q and a document D consisting of a list of sentences, in which one of the sentences contains the answer to the question.

we define: the sentences are divided into two subsets; those that contain the answer to q should be similar to q, while those that do not should be dissimilar.


The unification operation is useful for other NLP tasks, for instance, combining information from two different annotation schemes or enriching one annotation scheme with information from a different one.

we can take advantage of the new framework to enrich the node structure reflected in one theory with grammatical functions reflected in an annotation scheme that follows a different theory.


(2009) or Huang and Sagae (2010) are accurate and efficient, they can be trained from annotated data without the need for a grammar, and they provide a simple representation of syntax that maps to predicateargument structure in a straightforward way.

transition-based dependency parsers (Nivre, 2008) are a type of dependency parsing algorithms which use a model that scores transitions between parser states.


The reconstruction process consists of finding a minimum branching (i.e.

a directed minimum spanning tree) for a weighted directed graph obtained from assigning a cost c(i, j) to each arc (i, j) of the following directed graph: we will find a dependency tree T = (V w , A T ⊆ A(U )) such that the sum of costs of the arcs in A T is minimal.


At this time, no information about the object of with is available; with fails to provide what we call a complete factor for the calculation of the scores of the alternative transitions under consideration.

the model cannot make use of any evidence to distinguish between the two examples in Figure 1, and it is bound to get one of the two cases wrong.


This corpus helped both techniques to finish with an accuracy higher than 70%, and to predict over 85% of the answers clicked by users.

our results underline the importance of non-KB training data.


Consequently, we concluded that the information conveyed within the multiple snippets was often enough to answer the respective definition question.

a significant fraction of the users were satisfied with a small set of brief, but quickly generated descriptions.


We hypothesize that these prominent elements correspond to noninformative, and thus non-descriptive, content as they appear within snippets across several questions.

: "If it seems to answer every question, it will probably answer no question".


means of exploiting our negative set makes its positive contribution clear.

this supports our hypothesis that redundancy across websnippets pertaining to several definition questions can be exploited as negative evidence.


In light of these observations, we also conjecture that a more reduced, but diverse and manually annotated, corpus might be more effective.

a manually checked corpus distilled by inspecting click patterns across query logs of search engines.


As discussed in Sections 3 and 4, several oracle decoding algorithms can be expressed as shortestpath problems, provided a suitable definition of the underlying acceptor and associated semiring.

quantities such as: where the total weight of a successful path π = ξ 1 .


We attempt an entirely different approach; we use cheap and plentiful monolingual resources to induce an end-toend statistical machine translation system.

we extend the long line of work on inducing translation lexicons (beginning with Rapp (1995)) and propose to use multiple independent cues present in monolingual texts to estimate lexical and phrasal translation probabilities for large, MT-scale phrase-tables.


and γ k are the features of the model and their respective parametric weights.

four logarithmic features were considered for this sampling technique: a direct and an inverse IBM model 4 (Brown et al., 1994); and both, source and target, 5-gram language models.


Several conclusions can be drawn: • The translation quality provided by the infrequent n-grams technique is significantly better than the results achieved with random selection, comparing similar amount of sentences.

the improvements obtained are in the range of 3 BLEU points.


Our contributions are: This paper considers named entity recognition (NER) in text that is different from most past research on NER.

we consider Arabic Wikipedia articles with diverse topics beyond the commonly-used news domain.


Models characterized by parent-node and parent-node-filler tree representations have indeed the best performances, although the gain with respect to the other models is not as much as it could be expected given the difference in the oracle performances discussed above.

the best absolute performance is obtained with the model parent-node-filler.


These two participants P1 and P2, used a system based on CRF, and rules for deep syntactic analysis, respectively.

p 2 obtained superior performances in previous evaluation campaign on named entity recognition.


We do not take a strong "ontological" position on what events and fluents are, as part of our task these distinctions are made a priori.

events and fluents are input to our temporal linking framework.


This ability to easily train summarization models makes it possible to efficiently tune models to various types of document collections.

we find that our learning method can reliably tune models with hundreds of parameters based on a training set of about 30 examples.


In this section, we propose a supervised learning method for training a submodular scoring function to produce desirable summaries.

for the pairwise and the coverage model, we show how to learn the similarity function σ(i, j) and the word importance weights ω(v) respectively.


In particular, for the pairwise and the coverage model, we show how to learn the similarity function σ(i, j) and the word importance weights ω(v) respectively.

we parameterize σ(i, j) and ω(v) using a linear model, allowing that each depends on the full set of input sentences x: In the above equations, w is a weight vector that is learned, and φ p x (i, j) and φ c x (v) are feature vectors.


Figure 4 evaluates how effectively the learning algorithm can make use of a given amount of training data.

the figure shows the learning curve for our approach.


To understand which features affected the final performance of our approach, we assessed the strength of each set of our features.

we looked at how the final test score changes when we removed certain features groups (described in the beginning of Section 5) as shown in Figure 6.


Our approach contrasts with all previous models in assuming a very general kind of linguistic knowledge and a probabilistic grammar.

we use the probabilistic Combinatory Categorial Grammar (CCG) framework, and assume only that the learner has access to a small set of general combinatory schemata and a functional mapping from semantic type to syntactic category.


However, the learning approaches presented in those previous pa-pers are not designed to be cognitively plausible, using batch training algorithms, multiple passes over the data, and language specific initialisations (lists of noun phrases and additional corpus statistics), all of which we dispense with here.

our approach is closely related that of Kwiatkowski et al.


Of course, despite the incremental nature of our learning algorithm, there are still many aspects that could be criticized as cognitively implausi-ble.

it generates all parses consistent with each training instance, which can be both memory-and processor-intensive.


Hence, we use confidence estimation (Gandrabur and Foster, 2003;Blatz et al., 2004;Ueffing and Ney, 2007) to estimate the probability of correctness of the translations.

we estimate the quality of a translation from the confidence scores of their individual words.


The results in the experimentation show that the use of AL techniques allows us to make a tradeoff between required human effort and final translation quality.

we can adapt our system to meet the translation quality requirements of the translation task or the available human resources.


However, much research in Translation Studies indicates that the direction of translation matters, as translated language (translationese) has many unique properties.

phrase tables constructed from parallel corpora translated in the same direction as the translation task perform better than ones constructed from corpora translated in the opposite direction.


Furthermore, we show that the direction of translation used for producing the parallel corpus can be approximated by defining several entropybased measures that correlate well with translationese, and, consequently, with the quality of the translation.

we use the entire corpus, create a single, unified phrase table and then use the statistical measures mentioned above, and in particular cross-entropy, as a clue for selecting phrase pairs from this table.


We rely on features that are motivated by the findings of Translation Studies, having established their relevance through a comparative analysis of the phrase tables.

we use measures such as translation model entropy, inspired by Koehn et al.


Finally, for each target phrase t in the phrase table we compute the ratio of the perplexity of t according to the original language model L o and the perplexity of t with respect to the translated model L t (see Section 4).

the factor F is computed as follows: We apply these techniques to the French-to-English and English-to-French phrase tables built from the mixed corpora and use each phrase table to train an SMT system.


We plan to further explore the use of two phrase tables, one for each direction-determined subset of the parallel corpus.

we will interpolate the translation models as in , including a maximum a posteriori combination (Bacchiani et al., 2006).


It seems clear from Table 3 that some tasks benefit from these indicators more than others.

task C shows consistent improvements whereas task B is hardly affected.


The input to our system is a small set of manually-defined seed nouns for each event role.

the user is required to provide 10 role-identifying nouns for each event role.


Thus, the event narrative labeller only relies on the output of the two sentence classifiers.

we label a document as an event narrative if ≥ 50% of the sentences in the document are relevant (i.e., labeled positively by either sentence classifier).


However, the recall of the bootstrapped classifiers is consistently lower than the recall of the supervised classifiers.

the recall is about 10 points lower for three event roles (PerpInd, Target and Victim) and 20 points lower for the other two event roles (PerpOrg and Weapon).


It learns a pairwise ranking preference between alternative renderings of a document based on the probability distribution of those transitions.

b&L associated a lower rank with automatically created permutations of a source document, and learned a model to discriminate an original text from its permutations (see Section 3.1 below).


By contrast, we wish to refine the learning procedure in a way such that the resulting model will be able to evaluate coherence on a more fine-grained level.

we propose a concise extension to the standard entity-based coherence model by learning not only from the original document and its corresponding permutations but also from ranking preferences among the permutations themselves.


In this article, we analyze and compare techniques for constructing models that, given a partial chain of events, predict other events that belong to the script.

we consider the following questions: • How should representative chains of events be selected from the source text?


Following prior work (Chambers and Jurafsky, 2008;Chambers and Jurafsky, 2009;Manshadi et al., 2008;McIntyre and Lapata, 2009; McIntyre and Lapata, 2010), we focus on gathering statistics about the n-grams of events that occur in the collected event chains.

we look at strategies for collecting bigram statistics, the most common type of statistics gathered in prior work.


Second, our novel bigram probabilities ranking function outperforms the other ranking methods.

it outperforms the state-of-the-art pointwise mutual information method introduced by Chambers and Jurafsky (2008), and it does so by a large margin, more than doubling the Recall@50 on the Reuters corpus.


We have also discussed cost weighting in the context of ROC, and Hand (2009) seeks to improve on ROC AUC by introducing a beta distribution as an estimated cost profile, but we will not discuss them further here as we are more interested in the effectiveness of the classifer overall rather than matching a particular cost profile, and are skeptical about any generic cost distribution.

the beta distribution gives priority to central tendency rather than boundary conditions, but boundary conditions are frequently encountered in optimization.


In order to minimize the false negative instances from the data set, we did thread location filtering.

we want to filter out messages that actually interest the user but do not have the user's participation because they are not on the first page.


A Naive Bayes classifier is a generative model in which words inside a document are assumed to be conditionally independent.

given the class of a document, words are generated independently.


Thus POS N-grams predict word relations only in a probabilistic sense.

to transform Pr(R | r, t) into a form we can estimate, we first apply the definition of conditional probability: to estimate the numerator Pr(R, t, r), we first marginalize over the POS N-gram p: We expand the numerator using the chain rule: Cancelling the common factor yields: We approximate the first term Pr(R | p, t, r) as Pr(R | p), based on the simplifying assumption that R is conditionally independent of t and r, given p. we assume that given a POS N-gram, the target and relative words t and r give no additional information about the probability of a relation.


One likely cause of errors in case (b) is overgeneralization when PONG abstracts a word Ngram labeled with a relation by mapping its POS N-gram to that relation.

the coarse POS tag set may discard too much information.


On the other hand, our results show that the independent effects of wordbased PSG estimates only become apparent when investigating the effect of surprisal of the previous word.

considering only the current word's surprisal, as in Frank and Bod's analysis, did not reveal a significant contribution of PSGs over and above RNNs.


More generally, it turns out that when the rank of P equals the minimal number of states of an operator model that computes P, then one can prove a duality relation between operators and factorizations of P .

one can show that, for any rank factorization P = QR, the operators given byᾱ 1 = Q + p 1 ,ᾱ ∞ = p ∞ R + , andĀ a = Q + P a R + , yield an operator model for P. A key fact in proving this result is that the function P is invariant to the basis chosen to represent operator matrices.


The walk features extracted from MEDTs have the following properties: • The directionality of the edges (or nodes) in an e-walk (or v-walk) is not considered.

e.g., pos(stimulatory)−amod− pos(ef f ects) and pos(ef f ects) − amod − pos(stimulatory) are treated as the same feature.


A sentence is denoted by x = x 1 ...x k , where x i is the i-th word of x.

a coordination boundaries set is denoted by y = y 1 ...y k , where y i has a non-null value only when it is a coordinating conjunction.


(2009) proposed to use a contextfree grammar to find a properly nested coordination structure.

the scoring function Eq (1) COORD Coordination.


First, we start with the intuition that the assumption of symmetry strengthens the confidence of a translation pair.

if the most probable translation candidate for a source word w S 1 is a target word w T 2 and, vice versa, the most probable translation candidate of the target word w T 2 is the source word w S 1 , and their TI+Cue scores are above a certain threshold, we can claim that the words w S 1 and w T 2 are a translation pair.


As Gildea (2010) shows, there is no one to one correspondence between fan-out and parsing complexity: it is possible that parsing complexity can be reduced by increasing the fan-out of a production.

there can be a production which can be binarized with a parsing complexity that is minimal while its fan-out is sub-optimal.


However, task-based evaluation can be difficult and expensive for interactive applications.

task-based evaluation for dialogue systems typically involves collecting data from a number of people interacting with the system, which is time-consuming and labor-intensive.


Although the intrinsic evaluation shows that the BEETLE II interpreter performs better than the baseline on the F score, ultimately system developers are not interested in improving interpretation for its own sake: they want to know whether the time spent on improvements, and the complications in system design which may accompany them, are worth the effort.

do such changes translate into improvement in overall system performance?


To ensure portability to new, previously unseen annotations, the proposed method avoids encoding features specific to a particular domain or annotation.

the use of annotations' labels (composed-by) as lexical features might be tempting, but would anchor the annotation model to that particular annotation.


In this paper, we investigate whether the other potential advantage of incremental processingproviding "higher-level"-feedback to lower-level modules, in order to improve subsequent processing of the lower-level module-can be realised as well.

we experimented with giving a syntactic parser feedback about whether semantic readings of nominal phrases it is in the process of constructing have a denotation in the given context or not.


We show that such natural errors are better suited for evaluation than the previously used artificially created errors.

the precision of statistical methods has been largely overestimated , while the precision of knowledge-based approaches has been underestimated.


We show that evaluating measures of contextual fitness on this dataset provides a more realistic picture of task performance.

using artificial datasets over-estimates the performance of the statistical approach, while it underestimates the performance of the knowledgebased approach.


While recent research engages in specialized models and algorithms to improve the effectiveness of patent retrieval, we bring another aspect into focus: the detection and exploitation of patent inconsistencies.

we analyze spelling errors in the assignee field of patents granted by the United States Patent & Trademark Office.


Therefore, an important task in developing UBY-LMF has been the specification of attributes and their values along with the proper attachment of attributes to LMF classes.

this task involved selecting DCs from ISO-Cat and, if necessary, adding new DCs to ISOCat.


We also experiment with the addition of positional context word information, as commonly used in WSI.

we introduce an additional word feature for each of the three words to the left and right of the target word.


Thus our first-order character kernel c 1 is: We use c 1 and equation 1 to construct a firstorder plot kernel (which we call k 1 ), and also as an ingredient in a second-order character kernel c 2 which takes into account the curve of pairwise frequencies u, u between two characters u and u in the same novel.

u is similar to v if, for some relationships of u with other characters u , there are similar characters v who serves the same role for v. We use c 2 and equation 1 to construct our full plot kernel k 2 .


3 We treat this phenomena by splitting the component parts during training and remerging during generation.

for German, this requires splitting the words which have German POS tag APPRART into an APPR (preposition) and an ART (article).


Arabic morphology is complex, partly because of its richness, and partly because of its complex morpho-syntactic agreement rules which depend on functional features not necessarily expressed in word forms.

challenging are broken plurals (which resemble singular nouns), nouns with irregular gender (masculine nouns that look feminine and feminine nouns that look masculine), and the semantic feature of rationality, which has no morphological realization (Smrž, 2007b;Alkuhlani and Habash, 2011).


In experiments we have done, we found that training single classifiers and combining their outcomes almost always outperforms a single joint classifier for the three target features.

combining the results of G and N (G+N) outperforms the results of the single classifier GN.


Recent developments of large semantic resources have accelerated empirical research on semantic processing (Màrquez et al., 2008).

corpora with semantic role annotations, such as PropBank (Kingsbury and Palmer, 2002) and FrameNet (Ruppenhofer et al., 2006), are indispensable resources for semantic role labeling.


However, the problem with this approach is that DEOs are not the only words that co-occur with NPIs.

there exist many piggybackers, which, as defined by DLD09, collocate with DEOs due to semantic relatedness or chance, and would thus incorrectly receive a high S(y) score.


A better mathematical grounding of the distillation method's apparent heuristic in terms of existing probabilistic models sheds light on the mixed performance of distillation across languages and experimental settings.

it turns out that the distillation method of DLD09 is equivalent to one iteration of EM prior re-estimation in a Naïve Bayes model.


Thus, P (y, X y = 1) = P (y, X y = 1) + P (y, X y = 0) = P (y) One iteration of EM to calculate this probability is equivalent to the distillation method of DLD09.

the numerator of (17), which we just showed to be equal to the estimate of P (Y ) given by (16), is exactly the sum of the responsibilities for a particular y, and is proportional to the summation in (8) modulo normalization, because P ( X|y) is constant for all y in the context.


The key observation that allows us to resolve the tension between trusting the initialization and enforcing one DEO per NPI context is that the distributions of words that co-occur with DEOs and piggybackers are different, and that this difference follows from Ladusaw's hypothesis.

while DEOs may appear with or without piggybackers in NPI contexts, piggybackers do not appear without DEOs in NPI contexts, because Ladusaw's hypothesis stipulates that a DEO is required to license the NPI in the first place.


As evidence, we show that the heuristic algorithm is able to work in English with just the single seed NPI any, and in fact the bootstrapping approach in conjunction with our heuristic even outperforms the above approaches when using a static list of NPIs.

we use the methods described in the previous sections for Step (2a), and the following ratio to rank NPI candidates in Step (2b), corresponding to the baseline method to detect DEOs in reverse: Here, count D (x) refers to the number of occurrences of NPI candidate x in DEO contexts D, defined to be the words to the right of a DEO operator up to a comma or semi-colon.


Zero subjects (b) appear as the result of a nominal ellipsis.

a lexical element -the elliptic subject-, which is needed for the interpretation of the meaning and the structure of the sentence, is elided; therefore, it can be retrieved from its context.


We collected 100 sentence pairs of each type in French, for which various comparability measures are reported on Table 1.

the "% aligned tokens" row indicates the proportion of tokens from the sentence pairs that could be manually aligned by a native-speaker annotator.


Nonetheless, on average the techniques in our study produce more candidates that are not in the gold standard: this will be an important fact to keep in mind when tackling the task of combining their outputs.

we will investigate the use of features indicating the combination of techniques that predicted a given paraphrase pair, aiming to capture consensus information.


However, the constraints on possible reorderings are too restrictive in order to model all word movements required for English-to-German translation.

the reordering rules involve only the permutation of direct child nodes and do not allow changing of child-parent relationships (deleting of a child or attaching a node to a new father node).


For the development of the reordering rules, we used a small sample of the training data.

by observing the English parse trees extracted randomly from the training data, we developed a set of rules which transform the original trees in such a way that the English verbs are moved to the positions which correspond to the placement of verbs in German.


However, commercially successful car navigation systems continuously monitor whether the driver is following the instructions and provide modified instructions in real time when necessary.

two key problems in designing NLG systems for car navigation instructions are the availability of suitable map resources and the ability of the NLG system to generate instructions and react to driving errors in real time.


We then analyzed the gaze data collected by the table-mounted eye tracker, which we set up such that it recognized glances at the navigation screen.

we looked at the total fixation duration (TFD), i.e.


As a first step towards leveraging this broad range of language processes, we refer to one very specific topic, which has been referred to as entrainment, priming, accommodation, or adaptation in other computational work ).

we refer to the finding that conversational partners may shift their speaking style within the interaction, either becoming more similar or less similar to one another.


Consider, for instance, a joint distribution over a set of random variables n represent the set of n nodes in the network, representing the random variables that have a directed edge into a node v i .

then, by the dependencies specified by the network, any variable x i is directly dependent only on its parent variables, i.e.


We used the OPENSmile toolkit (opensmile, 2011) to compute the features.

within each turn the speech was segmented into analysis windows of 50ms, where adjacent windows overlapped by 40ms.


In reality, accommodation is a process of convergence -an accommodating speaker's speaking style may be expected to converge toward that of their partner.

the person's speaking style depends not only on whether they are accommodating or not, but also on their partner's style at the previous turn.


When we examine the two way interactions between DirectInfluence and RealVsConstructed as well as between IndirectInfluence and RealVsConstructed, although we do not find significant interactions, we do find some suggestive patterns when we do the student T posthoc analysis.

when we explore just the interaction between In-directInfluence links, we find a significant separation between Real vs Constructed pairs for models with Accommodation states, but not for the cases where no Accommodation states are included.


Poss is assigned to NPs involved in intra-phrasal possessive relations, including prenominal genitives (i.e., X's Y) and postnominal genitives (i.e., Y of X).

y will be marked as poss if X is old or med; otherwise, y will be new.


Note that both sources provide identity coreference chains.

the gold chains were annotated for NPs belonging to old/identity and old/ident generic.


In Rule 14, we use FrameNet (Baker et al., 1998) to determine whether med/situation should be assigned to an NP, NP i .

we check whether it fills an argument of a frame set up by a preceding NP, NP j , or verb.


In Rule 15, we use WordNet (Fellbaum, 1998) to determine whether med/event should be assigned to an NP, NP i , by checking whether NP i is related to an event, which is typically described by a verb.

we use WordNet to check whether there exists a verb, v, preceding NP i such that v and NP i have the same hypernym.


We create 17 binary features, 16 of which correspond to the 16 IS subtypes and the remaining one corresponds to a "dummy subtype".

if the NP un-der consideration appears in the training set, we use Rule 17 in our hand-crafted ruleset to determine the IS subtype it is most frequently associated with in the training set, and then set the value of the feature corresponding to this IS subtype to 1.


Table 3 shows the results of the two approaches.

row 1 shows their accuracy, which is defined as the percentage of correctly classified instances.


As we can see, the rule-based approach achieves accuracies of 66.0% (gold coreference) and 57.4% (Stanford coreference), whereas the learning-based approach achieves accuracies of 86.4% (gold) and 78.7% (Stanford).

the gold coreference results are better than the Stanford coreference results, and the learningbased results are better than the rule-based results.


While perhaps neither of these results are surprising, we are pleasantly surprised by the extent to which the learned classifier outperforms the handcrafted rules: accuracies increase by 20.4% and 21.3% when gold coreference and Stanford coreference are used, respectively.

machine learning has "transformed" a ruleset that achieves mediocre performance into a system that achieves relatively high performance.


These gains are accompanied by a simultaneous rise in recall and precision.


To gain some insight into the extent to which a knowledge source or a rule contributes to the overall performance of the rule-based approach, we conduct ablation experiments: in each experiment, we measure the performance of the ruleset after removing a particular rule or knowledge source from it.

rows 2-4 of Table 6 show the accuracies of the ruleset after removing the memorization rule (Rule 17), the rule that uses ReVerb's output (Rule 12), and the cue words used in Rules 4 and 10, respectively.


Here we consider the extended top-down tree transducer (XTOP), which was studied in (Arnold and Dauchet, 1982;Knight, 2007;Graehl et al., 2008;Graehl et al., 2009) and implemented in the toolkit TIBURON (May and Knight, 2006;May, 2010).

we investigate compositions of linear and nondeleting XTOPs (ln-XTOP).


Composition allows us to recombine those parts into one device modeling the whole translation.

it gives all parts the chance to vote at the same time.


If this is not already the case, then we can simply add useless states without rules for them.

we assume that the first case applies to each l ∈ L. Next, we add two sets of rules to R M , which will not change the semantics but prove to be useful in the composition construction.


First, for every tree t ∈ L, let R t contain all the rules p(l) → r, where p = p(l) → r is a new state with p ∈ P , minimal normalized tree l ∈ T Σ (X), and an instance r ∈ T ∆ (P (X)) of t such that Figure 12: Useful rules for the composition M ; N of Example 8, where s, s ∈ {α, β} and ρ ∈ P σ(z2,z3) .

m ξ ⇒ m r for some ξ that is not an instance of t. we construct each rule of R t by applying existing rules of R m in sequence to generate a (minimal) right-hand side that is an instance of t. We thus potentially make the right-hand sides of m bigger by joining several existing rules into a single rule.


Moreover, we identify trees of T Γ (Q (P (X))) with trees of T Γ ((Q × P )(X)).

when meeting a subtree q(p(x)) with q ∈ Q , p ∈ P , and x ∈ X, then we also view this equivalently as the tree q, p (x), which could be part of a rule of our composed XTOP.


The lexical description in modern English monolingual dictionaries (Sinclair et al., 1987;Rundell, 2002) explicitly emphasizes contextual clues, such as typical collocates and the syntactic surroundings of the given lexical item, rather than relying on very detailed definitions.

the sense definitions are obtained as syntactico-semantic abstractions of manually clustered corpus concordances in the modern corpus-based lexicography: in classical dictionaries as well as in semantic concordances.


It also allows us to introduce a new class of undirected features.

we introduce the Context-Free Factor (CFF) features.


We extend this work by modeling Minimum Translation Units with recurrent neural networks.

we introduce two recurrent neural network-based MTU models to address the is-  sues regarding data sparsity and limited context sizes by leveraging continuous representations and the unbounded history of the recurrent architecture.


Since this definition is fairly general it is thus suitable for most NLP tasks.

it is tailored to the type of segments that are suitable for the purposes of SMT and is in line with previous work (Blackwood et al., 2008;Paul et al., 2010).


The most widely used strategy for extracting high quality phrase-level translations without linguistic information, namely the consistency method (Och et al., 1999;Koehn et al., 2003) is entirely based on components of word aligned unsegmented sentence pairs (Martzoukos et al., 2013).

each extracted translation is either a component or the union of components.


A detailed account on the CE method is beyond the scope of this work, and we thus simply describe its application to our problem.

we first establish the connection between the most basic form of the CE method and the problem of finding the best monolingual segmentation of a sentence, with respect to some scoring function (not necessarily the one that was introduced in Section 2).


We assume no prior knowledge on the quality of bit-strings, so that they are all equally likely.

each position of a randomly chosen bit-string can be either a '0' or a '1' with probability 1/2.


In other words, each position of a randomly chosen bit-string can be either a '0' or a '1' with probability 1/2.

the aim is to tune these position probabilities towards the best bit-string, with respect to some scoring function g. let the sentence have n words and let = n − 1 be the length of bit-strings.


Nevertheless, we argue that the gangs forum we have selected as our data source provides a strategic source of data for exploring how social context influences stylistic language choices, in part because it is an area where the dual goals of predictive accuracy and interpretability are equally important.

evidence that gang related crime may account for up to 80% of crime in the United States attests to the importance of understanding the social practices of this important segment of society (Johnsons, 2009).


Our work focuses on analysis of stylistic choices that are influenced by social context, so it is important to consider some details about the social context of this forum.

we discuss which gangs are present in the data and how the gangs are organized into alliances and rivalries.


), emoticons, and URLs (Yan & Yan, 2006  In order to place ourselves in the best position to build an interpretable model, our space of graffiti style features was designed based on a combination of qualitative observations of the gangs forum data and reading about gang communication using web accessible resources such as informational web pages linked to the forum and other resources related to gang communication (Adams & Winter, 1997;Garot, 2007).

in our corpus we observed gang members using what we refer to as graffiti style features to mark their identity.


What exacerbates these problems in text processing approaches is that texts are typically represented with features that are at the wrong level of granularity for what is being modeled.

for practical reasons, the most common types of features used in text classification tasks are still unigrams, bigrams, and part-of-speech bigrams, which are highly prone to over-fitting.


A large part of the methodology for syntactic parsing has been developed for English but many other languages of the world are fundamentally different from English.

morphologically rich languages -the other end of the configurational spectrum -convey most sentencelevel syntactic information by morphology (i.e.


In recent years, approaches that aim at extracting a subset of all relations have achieved great success.

previous research (Carreras and Màrquez, 2005;Punyakanok et al., 2008;Che et al., 2010;Zapirain et al., 2010) focused on verb-argument structures, i.e., relations between a verb and its syntactic arguments.


Given a knowledge base K, some initial formal query q and a focus point p in that query, the reasoning services supported by Quelo's query logic framework (see (Guagliardo, 2009)) will compute a set of new queries rev(q) formed by adding, deleting and revising the current query q at point p. The task of the generator is then to produce a natural language sentence for each new formal query q ′ ∈ rev(q) which results from this revision process.

each time the user refines a query q to produce a new query q ′ , the system computes all revisions rev(q) of q ′ that are compatible with the underlying knowledge base using a reasoner.


Thus, (Franconi et al., 2010a) defines a query linearisation as a strict total order 2 on the query tree that satisfies the following conditions: • all labels associated with the edge's leaving node precede the edge label • the edge label is followed by at least one label associated with the edge's arriving node • between any two labels of a node there can only be (distinct) labels of the same node The specific linearisation adopted in Quelo is defined by the depth-first traversal strategy of the query tree and a total order on the children which is based on the query operations.

the labels of a node are ordered according to the sequence applications of the add compatible concept operation.


We conducted evaluation experiments designed to address the following questions: • Does the scoring mechanism appropriately capture the ordering constraints on the generated queries ?

does it ensure that the generated queries respect the strict total order of the query tree linearisation ?


q n where q i+1 is an increment of q i .

q i+1 is derived from q i by adding, removing or substituting to q i a concept or a relation.


Taking the average of the majority vote, we found that the judges evaluated the queries as non fluent in 50% of the cases and as unclear in 10% of the cases.

template based queries were found to be disfluent about half of the time and unclear to a lesser extent.


12 000 relation identifiers were extracted from the 200 ontologies and 13 syntactic templates were found to be sufficient to verbalise these relation identifiers (see (Trevisan, 2010) for more details on this evaluation).

in general, the extracted lexicons permit covering about 85% of the ontological data.


In this paper, we have shown that FB-LTAG and chart based surface realisation provide a natural framework in which to implement conceptual authoring.

we show that the chart based approach naturally supports the definition of an incremental algorithm for query verbalisation; and that the added fluency provided by the grammar based approach potentially provides for query interfaces that are better accepted by the human evaluators.


What percentage of sentential paraphrases are reachable under the model?

given a collection of sentence pairs (a i , b i ) and an SCFG G, where each pair of a and b are sentential paraphrases, how many of the pairs are in the language of G?


To calculate non-strict overlap, we ignore the identities of non-terminal symbols in the left-hand and right-hand sides of the rules.

two rules are considered equivalent if they are identical after all the non-terminal symbols have been replaced by one equivalent symbol.


Similar to WPM, we use IDF as the basis of our proxy for weighting n-grams according to the value their translations are expected to provide to translators.

we define the weight of an n-gram to be the sum of the IDF values for each constituent unigram that comprises the n-gram.


We therefore rewrite entities in a document's sequences of events to the variables {x, y, z, O} in a way that maintains all pairwise relationships between the held-out event and others.

if the held-out event shares an entity with another event, this remains true after rewriting.


We may also do the converse, adapting our multi-argument event system to the task of guessing the simpler pair events.

we infer a full multi-argument event and extract from it a (subject,verb) pair relating to a particular entity.


Operators To combine distributional vectors into a single-vector sentence representation, we use a representative set of methods from Mitchell and Lapata (2010).

we use vector addition, elementwise (Hadamard) product, Kronecker product, and circular convolution (Plate, 1991;Jones and Mewhort, 2007), which are de-fined as follows for two word vectors x, y: Repeated application of the Sum operation adds contexts for each of the words that occur in a phrase, which maintains (and mixes) any noisy parts of the component word vectors.


Our work is closest to Fountain and Lapata (2011) who also develop a model for inducing natural language categories.

they propose an incremental version of Chinese Whispers (Biemann, 2006), a randomized graph-clustering algorithm.


Since exact inference of the parameters of the BayesCat model is intractable, we use samplingbased approximate inference.

we present two learning algorithms, namely a Gibbs sampler and a particle filter.


(3) Is our incremental learning mechanism cognitively plausible?

does the quality of the induced clusters improve over time and how do the learnt categories differ from the output of an ideal batch learner?


The 68 books were chosen trying to balance the number of reviews (our units of analysis) related to suspect books which probably or surely received fake reviews, with the number of reviews hypothesized to be genuine in that we expected the authors of the books not to have bought reviews.

we put into the group of the suspect bookshenceforth SB -the reviews of the four books indicated by David Streitfeld.


3.1.1 Determining the class of reviews by majority voting The deception clues discussed in Section 2.3 were used in our first experiment to identify the class of each review using majority voting.

those clues were considered as independent predictors of the class; the class predicted by the majority of the annotators/clues was assigned to the review.


In other words, those clues were considered as independent predictors of the class; the class predicted by the majority of the annotators/clues was assigned to the review.

if 0, 1 or 2 deception clues were found, the review was classified as true; if there were 3 or 4, the review was considered false.


's algorithm (discussed in Sub-section 3.2.1), and only the n-grams with the highest IG values were selected to be taken as features of the vectors which represented the reviews.

the n-grams were collected according to the scheme shown in Table 2.


This kind of simulation allows to estimate the performance of a classifier which performs several times a task over random outputs whose distribution reflects that of real data.

for this experiment, since we had 236 reviews whose 50% were labeled as false, 100000 times we produced 236 random binomial predictions, having p = .5.


Moreover, not all of our non-linguistic cues may be found in all review scenarios, and therefore the applicability of our methods to all review scenarios will have to be investigated.

cluster is likely to be applicable to most review domains, Nickname and Unknown Purchase are Amazon features that may or may not be adopted by other services allowing users to provide reviews.


Zooming in on the relative reading levels of the paired unsimplified and simplified sentences, we wanted to determine for how many sentence pairs the sentence reading levels determined by our model are compatible with the pair's ranking.

we calculated the percentage of pairs (S, N ) in which the reading level of a simplified sentence (S) is identified as less than, equal to, or greater than the unsimplified (normal) version of the sentence (N ), i.e., S < N , S = N , and S > N .


"Ward" shows the evaluation of the clustering initialised with centroids found by hierarchical clus- 5 Verbs missing from the clustering reduce the maximum achievable cluster purity score.

6 we take the clustering result with the minimum intra-cluster distance (not the clustering result with the best performance on the gold standard).


In recent years, temporal processing has gained increasing attention within the NLP community, in particular since TempEval evaluation campaigns have been organized on this topic (Verhagen et al., 2007;Verhagen et al., 2010;UzZaman et al., 2013).

the classification of temporal relations holding between entities such as events and temporal expressions (timex) is crucial to build event timelines and to reconstruct the plot of a story.


The idea behind this is that if the user is writing about, for example, traveling, words like 'go', 'hotel', and 'see' are likely to be in the buffer and thus could be suggested quickly.

the systems learns about the user and the topic on the fly.


The context-insensitive module, on the other hand, can predict any word, as long as it has been used before, but knows nothing about fixed combinations.


Concerning the relation between number of tweets and Twitter users, many scholars have noticed that it follows a Pareto-distribution (Heil and Piskorski, 2009;Asur and Huberman, 2010;Rui and Whinston, 2012).

a small part of the Twitter users produce a large part of the tweets.


First, there is no explicit representation of LR states, and second, probabilities of actions are computed dynamically from a PCFG rather than retrieved as part of static transitions.

this is unlike some other early approaches to probabilistic LR parsing such as (Ng and Tomita, 1991).


The experimental results show that our linguisticallymotivated approach benefits verb error correction.

in order to determine the error type, we build on the notion of verb finiteness to distinguish between finite and non-finite verbs (Quirk et al., 1985), that correspond to Agreement and Tense mistakes (examples (1) and (2) above) and Form mistakes (examples (3) and (4) above), respectively (see Sec.